• Journal of the Korean Institute of Intelligent Systems
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• v.14 no.1
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• pp.9-14
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• 2004

Rational sense is affected by emotion. If we add the factor of estimated emotion by environment information into robots, we may get more intelligent and human-friendly robots. However, various sensory information and pattern classification are prescribed for robots to learn emotion so that the networks are suitable for the necessity of robots. Neural network has superior ability to extract character of system but neural network has defect of temporal cross talk and local minimum convergence. To solve the defects, many kinds of modular neural networks have been proposed because they divide a complex problem into simple several subproblems. The modular neural network, introduced by Jacobs and Jordan, shows an excellent ability of recomposition and recombination of complex work. On the other hand, the recurrent network acquires state representations and representations of state make the recurrent neural network suitable for diverse applications such as nonlinear prediction and modeling. In this paper, we applied recurrent network for the expert network in the modular neural network structure to learn data pattern based on emotional assessment. To show the performance of the proposed network, simulation of learning the environment and behavior pattern is proceeded with the real time implementation. The given problem is very complex and has too many cases to learn. The result will show the performance and good ability of the proposed network and will be compared with the result of other method, general modular neural network.

• Journal of the Institute of Convergence Signal Processing
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• v.22 no.4
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• pp.193-198
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• 2021

Recently, interests in the 4th industry have increased the level of demand for pest control by farmers in the field of rice farming, and the interests and use of agricultural pest control drones. Therefore, the diversification of agricultural control drones that spray high-concentration pesticides and the increase of agricultural exterminators due to the acquisition of national drone certifications are rapidly developing the agricultural sector in the drone industry. In addition, as detailed projects, an effective platform is required to construct large-scale big data due to pesticide management, exterminator management, precise spraying, pest control work volume classification, settlement, soil management, prediction and monitoring of damages by pests, etc. and to process the data. However, studies in South Korea and other countries on development of models and programs to integrate and process the big data such as data analysis algorithms, image analysis algorithms, growth management algorithms, AI algorithms, etc. are insufficient. This paper proposed an online drone pest control management information platform to meet the needs of managers and farmers in the agricultural field and to realize precise AI pest control based on the agricultural drone pest control processor using drones and presented foundation for development of a comprehensive management system through empirical experiments.

• Journal of Navigation and Port Research
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• v.46 no.3
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• pp.280-288
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• 2022

This study examined the diagnostics of abnormalities and faults of equipment, whose rotational speed changes even during regular operation. The purpose of this study was to suggest a procedure that can properly apply machine learning to the time series data, comprising non-stationary characteristics as the rotational speed changes. Anomaly and fault diagnosis was performed using machine learning: k-Nearest Neighbor (k-NN), Support Vector Machine (SVM), and Random Forest. To compare the diagnostic accuracy, an autoencoder was used for anomaly detection and a convolution based Conv1D was additionally used for fault diagnosis. Feature vectors comprising statistical and frequency attributes were extracted, and normalization & dimensional reduction were applied to the extracted feature vectors. Changes in the diagnostic accuracy of machine learning according to feature selection, normalization, and dimensional reduction are explained. The hyperparameter optimization process and the layered structure are also described for each algorithm. Finally, results show that machine learning can accurately diagnose the failure of a variable-rotation machine under the appropriate feature treatment, although the convolution algorithms have been widely applied to the considered problem.

• Journal of Appropriate Technology
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• v.6 no.2
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• pp.136-145
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• 2020

Agriculture is the backbone of the economy of most developing countries. In these countries, agriculture or farming is mostly done manually with little integration of machinery, intelligent systems and data monitoring. Irrigation is an essential process that directly influences crop production. The fluctuating amount of rainfall per year has led to the adoption of irrigation systems in most farms. The absence of smart sensors, monitoring methods and control, has led to low harvests and draining water sources. In this research paper, we introduce a 433 MHz Radio Frequency and 2G based Smart Irrigation Meter System and a water prepayment system for rural areas of Tanzania with no reliable internet coverage. Specifically, Ngurudoto area in Arusha region where it will be used as a case study for data collection. The proposed system is hybrid, comprising of both weather data (evapotranspiration) and soil moisture data. The architecture of the system has on-site weather measurement controllers, soil moisture sensors buried on the ground, water flow sensors, a solenoid valve, and a prepayment system. To achieve high precision in linear and nonlinear regression and to improve classification and prediction, this work cascades a Dynamic Regression Algorithm and Naïve Bayes algorithm.

• Journal of Environmental Impact Assessment
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• v.32 no.6
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• pp.545-555
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• 2023

This study aimed to predict the distribution density and potential habitat of domestic cats (Felis catus) in order to identify core distribution areas. It also aimed to overlay protected areas to identify priority areas for cat management. Kernel density estimation was used to determine the distribution density, and areas with high density were classified in Greater Seoul, Chungnam, Daejeon, and Daegu. Elevation, distance from the used area and roughness were identified as important variables in predicting potential habitat using the MaxEnt model. In addition, the classification of suitable and unsuitable areas based on thresholds showed that the predicted presence of habitat was more extensive in Seoul, Sejong, Daejeon, Chungnam, and Daegu. Core distribution areas were selected by overlapping high-density areas with suitable areas. Priority management areas were identified by overlaying core distribution areas with designated wildlife sanctuaries. As a result, Gyeonggi, and Chungnam have the largest areas. In addition, buffer zones will be implemented to effectively manage the core distribution area and minimize the potential for additional introductions in areas of high management priority, such as protected areas. These results can be used as a basis for investigating the status of the cat's habitat and developing more effective management strategies.

• Geophysics and Geophysical Exploration
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• v.13 no.1
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• pp.102-108
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• 2010

The internal structure of a river embankment must be delineated as part of investigations to evaluate its safety. Geophysical methods can be most effective means for that purpose, if they are used together with geotechnical methods such as the cone penetration test (CPT) and drilling. Since the dyke body and subsoil in general consist of material with a wide range of grain size, the properties and stratification of the soil must be accurately estimated to predict the mechanical stability and water infiltration in the river embankment. The strength and water content of the levee soil are also parameters required for such prediction. These parameters are usually estimated from CPT data, drilled core samples and laboratory tests. In this study we attempt to utilise geophysical data to estimate these parameters more effectively for very long river embankments. S-wave velocity and resistivity of the levee soils obtained with geophysical surveys are used to classify the soils. The classification is based on a physical soil model, called the unconsolidated sand model. Using this model, a soil profile along the river embankment is constructed from S-wave velocity and resistivity profiles. The soil profile thus obtained has been verified by geotechnical logs, which proves its usefulness for investigation of a river embankment.

• Journal of the Korea Convergence Society
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• v.10 no.3
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• pp.1-6
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• 2019

In modern society, lifestyle and individuality are important, and personalized lifestyle and patterns are emerging. The number of people with articulation diseases is increasing due to wrong living habits. In addition, as the number of households increases, there is a case where emergency care is not received at the appropriate time. We need information that can be managed by ourselves through accurate analysis according to the individual's condition for health and disease management, and care appropriate to the emergency situation. It is effectively used for classification and prediction of data using CNN in deep learning. CNN differs in accuracy and processing time according to the data features. Therefore, it is necessary to improve processing speed and accuracy for real-time healthcare. In this paper, we propose motion monitoring using Mask R-CNN for articulation disease management. The proposed method uses Mask R-CNN which is superior in accuracy and processing time than CNN. After the user's motion is learned in the neural network, if the user's motion is different from the learned data, the control method can be fed back to the user, the emergency situation can be informed to the guardian, and appropriate methods can be taken according to the situation.

• The Journal of the Convergence on Culture Technology
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• v.8 no.5
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• pp.207-218
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• 2022

This study, to confirm the mediating effect of knee pain on cognitive functions and depression in older adults, and as an interdisciplinary research between the physical and psychological mechanisms, confirmed the identifying group differences by gender, age, and educational attainment of older adults, and aimed to research the improvement of cognitive functions, which is a main factor of dementia's risk prediction. The analysis data was from the 8th Korean Longitudinal Study of Ageing (KLoSA) in 2020, and the research model was verified using Process macro and model #4. The main analysis results are as follows. First, depression partially mediation effect of knee pain on cognitive functions. Second, the mediation effect of depression by gender was significant, but the direct effect in the male older adults group was twice that in the female older adults; the indirect effect did not vary significantly based on gender. Third, the mediating effect of depression by age was relatively greater in the old-old aged group than in the young-old aged one. Fourth, as for the mediating effect of depression according to the classification of educational attainment, the mediating effect was not significant in the group with a college degree or higher education but was significant in the remaining three sub-groups. Based on the results, this study makes implications for the need for active intervention strategies to improve cognitive functions, focusing on group differences by gender, age, and educational attainment in the management of knee pain and depression.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2008.05a
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• pp.967-970
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• 2008

The liver cancer in our country cancerous occurrence frequency to be the gastric cancer in the common cancer, to initially at second unique condition or symptom after the case which is slowly advanced without gets condition many the case which will be diagnosed in the liver cancer, most there was not a reasonable treatment method especially and if what kind of its treated and convalescence of the patient non quantity one, the case which will be discovered in early rising the treatment record was considered seriously about under the early detection. The system which it sees with the system for the early detection of the liver cancer reacts the blood of the control group other than the patient who is confirmed as the liver cancer and the liver cancer to the bio chip and bio chip Profiles mechanical studying leads and it is a system which it classifies. 1149 each other it reacted blood samples of the control group other than the liver cancer patient who is composed of the total 50 samples and the liver cancer which is composed of 100 samples to the bio chip which is composed with different oligo from the present paper and it was a data which it makes acquire worker the neural network it led and it analyzes the classification efficiency of the result $92{\sim}96%$ which it was visible.

• Composites Research
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• v.35 no.5
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• pp.317-321
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• 2022

In this study, the PIC design method with machine learning that automatically assigning different stacking sequences according to loading types was applied bumper beam. The input value and labels of the training data for applying machine learning were defined as coordinates and loading types of reference elements that are part of the total elements, respectively. In order to compare the 2D and 3D implementation method, which are methods of representing coordinate value, training data were generated, and machine learning models were trained with each method. The 2D implementation method is divided FE model into each face and generating learning data and training machine learning models accordingly. The 3D implementation method is training one machine learning model by generating training data from the entire finite element model. The hyperparameter were tuned to optimal values through the Bayesian algorithm, and the k-NN classification method showed the highest prediction rate and AUC-ROC among the tuned models. The 3D implementation method revealed higher performance than the 2D implementation method. The loading type data predicted through the machine learning model were mapped to the finite element model and comparatively verified through FE analysis. It was found that 3D implementation PIC bumper beam was superior to 2D implementation and uni-stacking sequence composite bumper.